Exploring Global Water Scarcity Dynamics through Causal Discovery and
Structural Causal Modelling
Abstract
Water scarcity represents a critical global challenge, which is driven
by diverse complex interactions between natural and anthropogenic
factors. Long-term water scarcity often results in depletion of water
resources in so-called water scarcity hotspots. To understand the
interactions among social, ecological and hydrological components within
water scarce systems at such hotspots, we applied causal discovery to
observational time series of socio-economic, meteorological, and
ecological variables. This resulted in a network representing the causal
relations between these variables and Terrestrial Water Storage (TWS).
Recognizing the limitations of causal discovery, we supplemented the
network with expert knowledge. From this we derived Structural Causal
Models (SCMs) that simulate the causal mechanisms influencing TWS trends
at the water scarcity hotspots. The resulting SCMs have a variable
performance with a median r^2 of 0.52 compared to TWS observations.
The SCMs allowed us to estimate the impact of anthropogenic and natural
changes on TWS variability at water scarcity hotspots. Our analysis
identified population dynamics as the most significant cause of TWS
change in hotspots. As such, this study demonstrates how causal
discovery and SCMs can enhance modelling of human-water system dynamics
affected by water scarcity, improving the understanding of these systems
and potential impacts of future changes on water storage and
availability. For future research, more detailed data on human-water use
is needed to improve the robustness of these models. This is essential
for developing effective water management strategies to mitigate water
scarcity at hotspots.